1. Field of the Invention
A system and method for object classification based upon the fusion of a radar system and a natural imaging device, and a sparse code representation of an identified object.
2. Description of the Prior Art
Sensor fusion object classification systems are known and well documented. Such systems will gather information from an active and passive sensor and associate the two data to provide the user with information relating to the data, such as whether the object is a vehicle or a non-vehicle. Such an association is commonly referred to as fusion and is referred to as such herein. In operation, fusion relates to return of a natural image captured by the passive sensor with respect to the detection of an object by the active sensor. Specifically, an active sensor such as a radar system will be paired to a passive sensor such as a video camera, and the objects detected by the radar will be mapped to the video image taken by the video camera. The fusion of such data may be done using algorithms which map the radar return to the video image. The fused data may then be further processed for relevant information such as object detection and classification using some form of visual graphic imaging interpretation. However, visual graphic imaging interpretation requires sufficient memory to store the visual graphic data, and sufficient processing speed to interpret the visual data in a timely manner. For example, U.S. Pat. No. 6,834,232 to Malhotra teaches the use of multiple sensor data fusion architecture to reduce the amount of image processing by processing only selected areas of an image frame as determined in response to information from electromagnetic sensors. Each selected area is given a centroid and the center of reflection for each detected object is identified. A set of vectors are determined between the centers of reflection and the centroid. The difference between the centers of reflection and the centroids are used to classify objects. However, Malhotra does not teach the use of orientation selective filters for object classification.
U.S. Pat. No. 6,889,171 to Skrbina et al. discloses a system fusing radar returns with visual camera imaging to obtain environmental information associated with the vehicle such as object classification. Specifically, a radar is paired with a camera and the information received from each are time tagged and fused to provide the user with data relating to object classification, relative velocity, and the like. This system requires the data to be processed through an elaborate and complicated algorithm and thus requires a processor with that ability to process a tremendous amount of data in a relatively short period of time in order to provide the user with usable data.
U.S. Pat. No. 7,209,221 to Breed et al. discloses a method of obtaining information regarding a vehicle blind spot using an infrared emitting device. Specifically, the method uses a trained pattern recognition technique or a neural network to identify a detected object. However, Breed et al. is dependent upon the trained pattern recognition technique whereby the amount of patterns and processes may place a huge burden on the system.
Accordingly, it is desirable to have a system for object classification which does not require the amount of processing capabilities as the prior art, and which can refine and improve its classification over time. One form of object recognition and classification is known as sparse code representation. It is understood that sparse coding is how the human visual system efficiently codes images. This form of object recognition produces a limited response to any given stimulus thereby reducing the processing requirements of the prior art systems. Furthermore, the use of sparse code recognition allows the system to be integrated with current systems having embedded radar and camera fusion capabilities.